分享一套用Pyecharts绘制RNA Seq火山图的代码
一直以来都比较习惯使用matplotlib绘制各种图像，但是在RNA Seq数据的展示中，静态图像实在是无法将信息全部有效展示。之前偶然间遇到了pyecharts，实在惊艳于echats图的漂亮，因此这段时间再次遇到需要绘制火山图的时候，就整理了一份绘制动态火山图的代码。
1、首先是引包
import pandas as pd
import pyecharts.options as opts
from pyecharts.charts import Scatter
import numpy as np
from pyecharts.commons.utils import JsCode
2、随后读入的数据，是DESeq2的输出文件，并做一些数据的初始化计算
data = pd.read_csv('DESeq2_output_file.csv')
data['-log10padj'] = data['padj'].apply(lambda x:-np.log10(x))
data['-log10pval'] = data['pval'].apply(lambda x:-np.log10(x))
x_axis_name = 'log2FoldChange'
y_axis_name = '-log10pval'
3、上调、下调、非显著的点需要标记不同的颜色，需要写一个函数提前计算好标记点的颜色
def set_color(data, y_threshold=-np.log10(0.05), x_left_threshold=-1, x_right_threshold=1):
x = data[x_axis_name]
y = data[y_axis_name]
if y<y_threshold:
return "gray"
else:
if x < x_left_threshold:
return "blue"
elif x > x_right_threshold:
return "red"
else:
return "gray"
并且需要调用该函数计算好color列
data['color'] =data.apply(lambda row:set_color(row), axis=1)
4、准备一个数据转化的接口，使pandas Dataframe转化为pyecharts可以读取的数据
def get_ydata(data, color):
return [list(z) for z in zip(
data[data['color']==color][y_axis_name].tolist(),
data[data['color']==color][x_axis_name].tolist(),
data[data['color']==color]['Name'].tolist(),
data[data['color']==color]['Description'].tolist(),
data[data['color']==color]['UniProtAC'].tolist())
]
5、最后一步，调用pyecharts绘制火山图
(
Scatter(init_opts=opts.InitOpts(width="100%", height="720px"))
.add_xaxis(
xaxis_data=data[data['color']=='gray'][x_axis_name].tolist(),
)
.add_yaxis(
series_name="gray",
color='gray',
y_axis=get_ydata(data, 'gray'),
symbol_size=2,
label_opts=opts.LabelOpts(is_show=False),
)
.add_xaxis(
xaxis_data=data[data['color']=='red'][x_axis_name].tolist(),
)
.add_yaxis(
series_name="red",
color='red',
y_axis=get_ydata(data, 'red'),
symbol_size=5,
label_opts=opts.LabelOpts(
is_show=True,
formatter=JsCode("function(params){return params.value[3]}"),
position="right",
),
)
.add_xaxis(
xaxis_data=data[data['color']=='blue'][x_axis_name].tolist(),
)
.add_yaxis(
series_name="blue",
color='blue',
y_axis=get_ydata(data, 'blue'),
symbol_size=5,
label_opts=opts.LabelOpts(
is_show=True,
formatter=JsCode("function(params){return params.value[3]}"),
position="left",
),
)
.set_global_opts(
tooltip_opts=opts.TooltipOpts(
is_show=True,
formatter=JsCode(
"function(params){return params.value[3] + '-'+ params.value[5] +'<br/> FDR: ' + params.value[1]+'<br/> log2FC: ' + params.value[2]+'<br/>' + params.value[4];}"
)
),
title_opts=opts.TitleOpts(
title="Volcano Plot Title",
pos_left="center",
),
legend_opts=opts.LegendOpts(is_show=False),
yaxis_opts=opts.AxisOpts(
name = y_axis_name,
type_= 'value',
),
xaxis_opts=opts.AxisOpts(
name = x_axis_name,
name_location='middle',
type_= 'value',
name_gap=20,
max_=8,
min_=-8,
),
datazoom_opts=[
opts.DataZoomOpts(
pos_left='5px',
orient='vertical',
range_start=0,
range_end=100,
),
opts.DataZoomOpts(
pos_bottom='5px',
range_start=0,
range_end=100,
)
],
toolbox_opts=opts.ToolboxOpts(is_show=True)
)
.set_series_opts(
markline_opts=opts.MarkLineOpts(
data=[
{"xAxis": -np.log2(2)},
{"xAxis": np.log2(2)},
{"yAxis":-np.log10(0.05)}
],
# label_opts=opts.LabelOpts(position="end"),
is_silent=True,
symbol_size=0,
linestyle_opts=opts.LineStyleOpts(color="gray", type_='dashed')
)
)
.render(f"volcano_plot_output.html")
)
